Abstract: The present disclosure provides methods, apparatuses, and computer-readable media for step discovery and localization in an instructional video. In some embodiments, the method includes extracting, from the instructional video using a transformer model, a plurality of step slots corresponding to a plurality of procedure steps depicted in the instructional video, matching, using an order-aware sequence-to-sequence alignment model, a plurality of video segments of the instructional video to the plurality of step slots, generating a temporally-ordered plurality of video segments from the plurality of video segments, receiving a user query requesting a procedure step, selecting, from the plurality of video segments of the instructional video, a corresponding video segment corresponding to the requested procedure step, and providing, in response to the user query, the corresponding video segment and the matching textual step description of the corresponding video segment.

Abstract: The present disclosure provides methods, apparatuses, and computer-readable mediums for performing data augmentation. In some embodiments, a method of performing data augmentation by a device includes obtaining a plurality of images from a dataset. The method further includes computing, for each image of the plurality of images, a corresponding saliency map based on a gradient of a full loss function of that image. The method further includes selecting, from a subset of arrangements of a plurality of possible arrangements, a rearrangement offset that maximizes an overall saliency of a resulting image combining the plurality of images. The method further includes generating, using the rearrangement offset and a plurality of mixing ratios, a new mixed image from the plurality of images and a new mixed label from corresponding labels of the plurality of images. The method further includes augmenting the dataset with the new mixed image and the new mixed label.

Abstract: A first graph which is a flow graph is converted into a second graph using a first algorithm. The second graph, combined with a second algorithm using a dynamic programming recursion, is compared with a video and a best matching thread, with respect to the video, is found through the second graph. The best matching thread may then be used for a user-assistance function. The dynamic programming recursion reduces computational effort needed by the computer performing the matching.